The clustering of wind turbines in a wind farm results in overall efficiency losses as downstream wind turbines operate in the wake of their upstream neighbours. Wind farm flow control (WFFC) strategies have emerged to reduce these wake effects with the goal of maximising overall performance. Dynamic induction control (DIC) aims to enhance the wake breakdown and restore the wake's energy content through dynamic thrust variations. The control signals are often found through the economic model predictive control (EMPC) method, which relies on an internal model to incorporate future system behaviour in the determination of the next optimal control input. These models are designed to capture the most dominant wake characteristics while remaining computationally efficient. We employ the two-dimensional free-vortex wake (FVW) model presented from [1], which models the wake through vortex element pairs released from the edges of the actuator disc. The power of the two-turbine wind farm is maximised through EMPC, improving performance by 9.64\% over greedy control simulations. However, the EMPC method inherits finite horizon effects, resulting in large control horizons to optimise. In this study, we address these limitations by employing an absolute sine parameterisation in the FVW model to limit the finite horizon effects and reduce the dimension of the optimisation problem. The significant dimension reduction allows for a grid search to find the optimal infinite horizon steady-state solution, improving the mean steady-state performance by 2.43\% over the baseline results from [1]. Additionally, we focus on converging towards this optimum through finite horizon EMPC optimising over the amplitude and offset. Grid search analyses reveal sensitivity towards initialisation due to the appearance of local minima around the infinite horizon optimum. A maximum success rate is realised for very large control horizons, maximising the probability of converging towards the infinite horizon optimum. Accounting for the inherited system delay in the objective function also realises a maximum success rate but for shorter control horizons, which significantly decreases the simulation time. The final controller design terminates simulations ten times faster through the absolute sine parameterisation compared to the baseline simulation from [1] while maximising the probability of convergence towards the infinite horizon optimum.

With the increasing water depths of the new offshore wind farms, the challenging soil conditions, the availability of assets, and other factors, jack-up installation vessels may no longer be suitable to complete the installation scope of work for the new wind farms. Therefore, installation methods and techniques using floating vessels must be further developed to allow safe and efficient installation of wind turbines.

Due to the response of floating installation vessels, excessive motions can be transmitted to the lifted object, making the installation operation very weather sensitive. To increase workability, Heerema Marine Contractors (HMC) has developed the RNA method for wind turbine installation using a semi-submersible crane vessel. This method uses a temporary support structure on the vessel deck (installation tower) where the RNA is fully assembled with the assistance of the GREP (Guided Root End Positioning) tool to constrain the blade root to the top of the installation tower and consequently the hub. Finally, the RNA is installed in a single lift on the WTG permanent support structure. However, the GREP tool is compatible only with a specific range of wind turbine blade dimensions; therefore, for a different-size wind turbine blade a new GREP tool must be designed, fabricated, and mobilised.

This project proposes an improvement to the HMC's RNA method to eliminate the necessity of the GREP tool. That is, a motion-compensated Stewart platform attached to the crane boom, where the blade is fixed to be installed in the hub on top of the installation tower. The project is developed by investigating the initial assumptions; those are crane boom stiffness, blade deflection, installation tower motions and their influence on the vessel's response, and aerodynamic loads acting on the blade during installation. The kinematics (inverse and forward) and dynamics of a Stewart platform are formulated, as well as the mechanical concept of the proposed system and the blade installation process using a Stewart platform attached to the crane boom.

Furthermore, to eliminate the requirement of the GREP tool, a control system is developed to compensate for the blade root motions relative to the hub. The motion control system uses sensors to measure the hub's motions and generate the Stewart platform actuators' set points. Different possible sensor set-ups are evaluated, and a filter is designed to reduce the influence of the sensors' noise. The control system is developed on the basis of feedback PI (Proportional and Integral) and adaptive feedforward control to the actuators (hydraulic cylinders).

It is concluded that it is technically feasible to use a motion- compensated Stewart platform for blade installation in the RNA method. However, the economic aspects of the proposed solution must be investigated.

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

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Wind turbine partial-load controllers have evolved from simple static nonlinear function implementations to more advanced dynamic controller structures. Such dynamic control schemes have the potential to improve power production performance in realistic environmental conditions and allow for a more granular trade-off between loads and energy capture. The control structure generally consists of a wind speed estimator (WSE) combined with a controller aiming to track the commanded tip-speed ratio (TSR) reference. The performance and resulting closed-loop system stability are however highly dependent on the accuracy of the internal model in the WSE-TSR tracking scheme. Therefore, developing learning algorithms to calibrate the internal model is of particular interest. Previous works have proposed such algorithms; however, they all rely on the availability of (rotor-effective) wind speed measurements. For the first time, this paper proposes an excitation-based learning algorithm that exploits the closed-loop dynamic structure of the WSE-TSR tracking scheme. This algorithm calibrates the internal model without the need for wind speed measurements. Analysis and simulations show that the proposed algorithm corrects for model uncertainties in the form of magnitude scaling errors under ideal constant and realistic turbulent wind conditions.@en

Underwater position estimation is challenging due to the absence of Global Navigation Satellite System (GNSS) signals. Underwater vehicles are typically equipped with a Doppler Velocity Log (DVL) that measures the velocity relative to the seafloor. Aside from the velocity, the DVL also measures the range of each of the four beams. When compared against a bathymetry height map, these measured ranges provide additional information enabling improving position estimates. Unfortunately, surveys often occur in areas where detailed bathymetry maps are unavailable. In these cases, bathymetry Simultaneous Localization and Mapping (SLAM) could be used to improve the position estimates compared to the velocity integration position. With SLAM, the map is being estimated during the mission while at the same time using the map for position determination.

In this thesis, reduced rank Gaussian processes (GPs) are used as map representation for SLAM. The downside of regular GPs is a time complexity of O(n^3) , reduced rank GPs improve the computation performance. GPs provide Gaussian distributions at any point, leading to a neat integration with probabilistic SLAM algorithms. To the best of the author’s knowledge, this has not been used in bathymetry SLAM. This report investigates how reduced rank approximated GPs, representing the bathymetry, can be integrated into a SLAM algorithm to improve the position estimates of an underwater vehicle equipped with a DVL and low-quality gyroscopes.

A squared exponential kernel is used as GPs model of the bathymetry. The reduced rank approximation is vital for real-time SLAM performance. This map representation is integrated with a Rao Blackwellized particle filter (RBPF) that estimates both the underwater vehicle’s trajectory and the bathymetry map.

The SLAM algorithm is evaluated using data from an underwater vehicle operated at the surface such that a GNSS reference position is available. Experiments of the SLAM algorithm show a reduced position error compared to the GNSS reference. The resulting algorithm has a computation time of up to 30 times faster than the Autonomous Underwater Vehicle (AUV) collects data while improving position estimates. This concludes that the RBPF using reduced rank GPs is capable of onboard improved position estimation on underwater vehicles.

Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model. This paper presents a new Instrumental Variable (IV) approach to Data-enabled Predictive Control (DeePC) that results in favorable noise mitigation properties, and demonstrates the direct equivalence between DeePC and Subspace Predictive Control (SPC). The methodology relies on the derivation of the characteristic equation in DeePC along the lines of subspace identification algorithms. A particular choice of IVs is presented that is uncorrelated with future noise, but at the same time highly correlated with the data matrix. A simulation study demonstrates the improved performance of the proposed algorithm in the presence of process and measurement noise.@en

Wind farm control using dynamic concepts is a research topic that is receiving an increasing amount of interest. The main concept of this approach is that dynamic variations of the wind turbine control settings lead to higher wake turbulence, and subsequently faster wake recovery due to increased mixing. As a result, downstream turbines experience higher wind speeds, thus increasing their energy capture. In dynamic induction control (DIC), the magnitude of the thrust force of an upstream turbine is varied. Although very effective, this approach also leads to increased power and thrust variations, negatively impacting energy quality and fatigue loading. In this paper, a novel approach for the dynamic control of wind turbines in a wind farm is proposed: using individual pitch control, the fixed-frame tilt and yaw moments on the turbine are varied, thus dynamically manipulating the wake. This strategy is named the helix approach because the resulting wake has a helical shape. Large eddy simulations of a two-turbine wind farm show that this approach leads to enhanced wake mixing with minimal power and thrust variations.

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In the last decades, tremendous efforts have been put in advancements of wind turbine technology by scientific research and industrial developments. One of the focal areas has been the upscaling of turbines to increase power capacity. However, by enlarging turbine sizes, the square-cube law dictates rising costs per unit of power capacity. To break this trend of increased expenses, more advanced control techniques are key in facilitating load reductions and system level advances. The synthesis of novel controller designs, and advancements of existing strategies, are in this thesis effectuated by leveraging well-established control theory. This method resulted in analysis tools, that gave rise to practical applicable implementations, of which some are evaluated on real-world setups. The employed approach has thereby shown to stimulate further advancements of wind turbine technology...@en

Standardized, easy to use, and preferably open-source research software is an important aspect in supporting and solidifying the wind turbine community. To this end, three contributions in the form of open-source software projects are presented in this paper. First, a community-driven wind turbine baseline controller, the Delft Research Controller (DRC), is presented. The DRC is applicable to high-fidelity simulation software that uses the DISCON controller interface. The controller distinguishes itself by the variety of available control and estimation implementations, its ease of use, and the universal applicability to wind turbine models. Secondly, in the wake of the DRC, the SimulinkDRC graphical controller design and compilation environment has been developed. Users having access to Simulink can benefit from the convenient way of controller development the tool provides. Finally, the FASTTool has been developed for educational purposes, by focusing on the graphical aspect of wind turbine (controller) design. The tool simplifies interaction with the advanced FAST simulation software, by comprehensive visualizations and analysis tools. This paper demonstrates and describes the functionality of all three software projects.@en

With the trend of increasing wind turbine rotor diameters, the mitigation of blade fatigue loadings is of special interest to extend the turbine lifetime. Fatigue load reductions can be partly accomplished using individual pitch control (IPC), and is commonly facilitated by the so-called multiblade coordinate (MBC) transformation. This operation transforms and decouples the blade load signals in a non-rotating yaw-axis and tilt-axis. However, in practical scenarios, the resulting transformed system still shows coupling between the axes. To cope with this phenomenon, earlier research has shown that the introduction of an additional MBC tuning variable-the azimuth offset-decouples the multivariable system. However, the introduction of this extra variable complicates the controller design process, and requires expert knowledge and specialized analysis software. To provide an efficient method for the optimization of fixed-structure IPC controllers, based on black box and computationally costly objective functions, this paper considers a Bayesian optimization controller tuning framework. Results show the efficiency of the framework to tune a combined 1P + 2P IPC implementation, without prior knowledge, and based on high-fidelity simulation results using a computationally expensive objective function.

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Wind energy research groups from various disciplines generally use self-developed baseline wind turbine control implementations and tunings, which complicates the evaluation and comparison of new control algorithms. To solve this problem, the Delft Research Controller (DRC) provides an open, modular and fully adaptable baseline wind turbine controller to the scientific community. New control implementations can be added to the existing baseline controller, and in this way, convenient assessments of the proposed algorithms is possible. Because of the open character and modular set-up, scientists are able to collaborate and contribute in making continuous improvements to the code. The DRC is being developed in Fortran and uses the Bladed-style DISCON controller interface. The compiled controller is configured by a single control settings parameter file, and can work with any wind turbine model and simulation software using the DISCON interface. Baseline parameter files are supplied for the NREL 5-MW and DTU 10-MW reference wind turbines.

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The growing importance of renewable energy to meet the demands of growing population has driven much focus for research in the wind energy sector. Currently most of the power production in the wind energy sector is done using the Horizontal Axis Wind Turbine (HAWT). due to its higher efficiency and reliability as compared to Vertical Axis Wind Turbine (VAWT). However, due to limitations of HAWTs such as the complex yaw mechanism, higher positioning of the center of mass and relatively difficult up-scaling, VAWT are receiving more attention.

Attempt to access high range of power from VAWTs at offshore locations may damage the turbine blades due to increased loads. This thesis is dedicated to reduce the periodic disturbances on the turbine blades of VAWTs without affecting the total power production in a rotation, thereby ensuring the reliability and safe operation of VAWT. A control technique called Subspace Predictive Repetitive Controller (SPRC) is used for the recursive identification to estimate the parameters of wind turbine model and further providing an optimal control law accordingly. Basis functions have been used to reduce the dimensionality of the system, following which the system identification has been performed in the lifted domain.

Using the identified model, two types of control approaches have been applied. In the first approach, the objective function is to track a specific reference that indicates blade load. This reference ensures a reduction in peak loads of the VAWT by transferring the loads from their upstream to their downstream part, without comprising on the power production in one whole rotation of VAWT. In the second approach, a general control strategy is used in which the controller is given freedom to choose the pitch trajectory so as to reduce the blade loads. The controller is specified with a power reference that ensures the maintenance of total power. This strategy allows the turbine to be operated in different wind conditions, as a higher weighting is assigned to power production when wind speeds are less than rated wind speeds, and a higher weighting is assigned to the reduction of blade load when wind speeds are more than the rated wind speeds. .

These results show a real potential of the data-driven SPRC approach in wind turbines, and highlights new mechanisms to reduce the turbine loads on \ac{VAWT}s. These mechanisms offer valuable insights into enhancing the functions of VAWTs in a more reliable and damage-free manner.